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OpenLearning

Introduction to Statistics

via OpenLearning

Overview

What you will be learning in this Program?

This course aims at exposing learners to the fundamental concepts of Statistics. Here, learners will be introduced to :

i. the definition of statistics and the various terminologies used in statistics,

ii. the purpose of charts in statistics,

iii. the basic statistical formulas

How is the Flow of this Program?

The mode of delivery starts with some reading notes on the topics, followed by a video lecture, a series of self-check exercises, and occasionally a discussion forum. In order to gauge the understanding of the topics, the learners will have to tick the self-check boxes which are relevant to the topics shared at the end of each session.

At any point, if the learners feel that they have not fully grasped the topic, they will be able to use the additional video aid to enhance their understanding of the topic.

Intended learner

This course adds a digital credential to the learners as they are able to upskill their knowledge in the field of statistics. Understanding the basics of Statistics is an important skill at present, as digital analytics, big data, data mining, predictive analytics are becoming major influential skills and knowledge that is transparent in many fields, such as economics, healthcare, banking, engineering, and etc.

The learners can be potential undergraduate or postgraduate students who intend to gain knowledge to help with their exams, credit transfer programs, and future jobs applications or professionals from the above-mentioned fields who want to keep themselves updated with the latest knowledge skill.This program will be able to cater to a wide range of market segments because it is a trending skillset knowledge. The systematic mode of delivery in this program creates an ease-of-use and user-friendly learning environment that will be able to cater to varied age groups.

Syllabus

  • Describe and differentiate the various terminologies used in statistics
  • Describe data distribution using graphs and numerical measures.
  • Compute the relative variability of random distributions.

Taught by

Centre for Academic Advancement and Flexible Learning

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